Infrared small target detection based on deep learning offers unique
advantages in separating small targets from complex and dynamic backgrounds.
However, the features of infrared small targets gradually weaken as the depth
of convolutional neural network (CNN) increases. To address this issue, we
propose a novel method for detecting infrared small targets called improved
dense nested attention network (IDNANet), which is based on the transformer
architecture. We preserve the dense nested structure of dense nested attention
network (DNANet) and introduce the Swin-transformer during feature extraction
stage to enhance the continuity of features. Furthermore, we integrate the
ACmix attention structure into the dense nested structure to enhance the
features of intermediate layers. Additionally, we design a weighted dice binary
cross-entropy (WD-BCE) loss function to mitigate the negative impact of
foreground-background imbalance in the samples. Moreover, we develop a dataset
specifically for infrared small targets, called BIT-SIRST. The dataset
comprises a significant amount of real-world targets and manually annotated
labels, as well as synthetic data and corresponding labels. We have evaluated
the effectiveness of our method through experiments conducted on public
datasets. In comparison to other state-of-the-art methods, our approach
outperforms in terms of probability of detection (P_d), false-alarm rate (F_a),
and mean intersection of union ($mIoU$). The $mIoU$ reaches 90.89 on the
NUDT-SIRST dataset and 79.72 on the NUAA-SIRST dataset.

提出了一种基于改进的稠密嵌套注意力网络（IDNANet）的红外小目标检测方法，采用变压器架构并结合 Swine-transformer 进行特征提取，在稠密嵌套结构中引入 ACmix 注意力结构增强中间层特征，通过设计加权 Dice 二元交叉熵（WD-BCE）损失函数缓解前景 - 背景样本不平衡的负面影响。通过在公共数据集上的实验表明，该方法在检测概率（P_d）、误警率（F_a）和平均交集联合（$mIoU$）等指标上优于其他最先进的方法，NUDT-SIRST 数据集上的 $mIoU$ 为 90.89，NUAA-SIRST 数据集上为 79.72。

基于 Transformer 的改进密集嵌套注意力网络用于红外小目标检测

Improved Dense Nested Attention Network Based on Transformer for  Infrared Small Target Detection

In oncology research, accurate 3D segmentation of lesions from CT scans is
essential for the modeling of lesion growth kinetics. However, following the
RECIST criteria, radiologists routinely only delineate each lesion on the axial
slice showing the largest transverse area, and delineate a small number of
lesions in 3D for research purposes. As a result, we have plenty of unlabeled
3D volumes and labeled 2D images, and scarce labeled 3D volumes, which makes
training a deep-learning 3D segmentation model a challenging task. In this
work, we propose a novel model, denoted a multi-dimension unified Swin
transformer (MDU-ST), for 3D lesion segmentation. The MDU-ST consists of a
Shifted-window transformer (Swin-transformer) encoder and a convolutional
neural network (CNN) decoder, allowing it to adapt to 2D and 3D inputs and
learn the corresponding semantic information in the same encoder. Based on this
model, we introduce a three-stage framework: 1) leveraging large amount of
unlabeled 3D lesion volumes through self-supervised pretext tasks to learn the
underlying pattern of lesion anatomy in the Swin-transformer encoder; 2)
fine-tune the Swin-transformer encoder to perform 2D lesion segmentation with
2D RECIST slices to learn slice-level segmentation information; 3) further
fine-tune the Swin-transformer encoder to perform 3D lesion segmentation with
labeled 3D volumes. The network's performance is evaluated by the Dice
similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D
lesion dataset with 593 lesions extracted from multiple anatomical locations.
The proposed MDU-ST demonstrates significant improvement over the competing
models. The proposed method can be used to conduct automated 3D lesion
segmentation to assist radiomics and tumor growth modeling studies. This paper
has been accepted by the IEEE International Symposium on Biomedical Imaging
(ISBI) 2023.

在肿瘤学研究中，准确的 CT 扫描病灶 3D 分割对于病灶生长动力学的建模至关重要。然而，根据 RECIST 准则，放射科医生通常仅在显示最大横截面面积的轴位切片上勾画每个病灶，并在研究目的上勾画少量的 3D 病灶。因此，我们有大量未标记的 3D 体积和带标签的 2D 图像，以及稀缺的标记的 3D 体积，这使得训练深度学习 3D 分割模型成为一项具有挑战性的任务。本研究提出了一种新模型，称为多维统一的 Swin Transformer (MDU-ST)，用于 3D 病灶分割。MDU-ST 由一个偏移窗口变换器 (Swin-transformer) 编码器和一个卷积神经网络 (CNN) 解码器组成，使其能够适应 2D 和 3D 输入，并在同一编码器中学习相应的语义信息。基于该模型，我们引入了一个三阶段框架：1) 通过自我监督的先验任务利用大量未标记的 3D 病灶体积来学习 Swin-transformer 编码器中病灶解剖学的潜在模式；2) 对 Swin-transformer 编码器进行微调，以使用 2D RECIST 切片执行 2D 病灶分割，学习切片级分割信息；3) 进一步对 Swin-transformer 编码器进行微调，以使用带标签的 3D 体积执行 3D 病灶分割。该网络的性能通过 Dice 相似系数 (DSC) 和 Hausdorff 距离 (HD) 在一个内部的 3D 病灶数据集上进行评估，其中包含来自多个解剖位置的 593 个病灶。所提出的 MDU-ST 相比竞争模型表现出显著改进。该方法可用于进行自动化的 3D 病灶分割以辅助放射组学和肿瘤生长建模研究。本论文已被 IEEE 国际生物医学成像研讨会 (ISBI) 2023 接受。